PEcnv: accurate and efficient detection of copy number variations of various lengths Article Swipe
Xuwen Wang
,
Ying Xu
,
R. Liu
,
Xin Lai
,
Yuqian Liu
,
Shenjie Wang
,
Xuanping Zhang
,
Jiayin Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbac375
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbac375
Copy number variation (CNV) is a class of key biomarkers in many complex traits and diseases. Detecting CNV from sequencing data is a substantial bioinformatics problem and a standard requirement in clinical practice. Although many proposed CNV detection approaches exist, the core statistical model at their foundation is weakened by two critical computational issues: (i) identifying the optimal setting on the sliding window and (ii) correcting for bias and noise. We designed a statistical process model to overcome these limitations by calculating regional read depths via an exponentially weighted moving average strategy. A one-run detection of CNVs of various lengths is then achieved by a dynamic sliding window, whose size is self-adopted according to the weighted averages. We also designed a novel bias/noise reduction model, accompanied by the moving average, which can handle complicated patterns and extend training data. This model, called PEcnv, accurately detects CNVs ranging from kb-scale to chromosome-arm level. The model performance was validated with simulation samples and real samples. Comparative analysis showed that PEcnv outperforms current popular approaches. Notably, PEcnv provided considerable advantages in detecting small CNVs (1 kb–1 Mb) in panel sequencing data. Thus, PEcnv fills the gap left by existing methods focusing on large CNVs. PEcnv may have broad applications in clinical testing where panel sequencing is the dominant strategy. Availability and implementation: Source code is freely available at https://github.com/Sherwin-xjtu/PEcnv
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbac375
- https://academic.oup.com/bib/article-pdf/23/5/bbac375/45936840/bbac375.pdf
- OA Status
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- Cited By
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- References
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- Related Works
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- OpenAlex ID
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- DOI
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https://doi.org/10.1093/bib/bbac375Digital Object Identifier
- Title
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PEcnv: accurate and efficient detection of copy number variations of various lengthsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-01Full publication date if available
- Authors
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Xuwen Wang, Ying Xu, R. Liu, Xin Lai, Yuqian Liu, Shenjie Wang, Xuanping Zhang, Jiayin WangList of authors in order
- Landing page
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https://doi.org/10.1093/bib/bbac375Publisher landing page
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https://academic.oup.com/bib/article-pdf/23/5/bbac375/45936840/bbac375.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://academic.oup.com/bib/article-pdf/23/5/bbac375/45936840/bbac375.pdfDirect OA link when available
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Computer science, AlgorithmTop concepts (fields/topics) attached by OpenAlex
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14Total citation count in OpenAlex
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2025: 7, 2024: 4, 2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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